code: def validate_scorers(scorers: t.List[str]):
      """Validate the given scorer list."""
      scoring_dict = get_scorer_dict()
  
      if not isinstance(scorers, Sequence):
          raise DeepchecksValueError(
              f"Scorers must be a Sequence, got {type(scorers)}"
          )
  
      for name in scorers:
          if not isinstance(name, str):
              # TODO: support custom scorers
              raise DeepchecksValueError(
                  f"Scorers must be a Sequence of strings, got {type(name)}"
              )
          if name not in scoring_dict:
              raise DeepchecksValueError(
                  "Scorers must be a list of names of existing token classification metrics, "
                  f"which is {scoring_dict.keys()}, got {scorers}"
              )
  
file_path: deepchecks/deepchecks/nlp/metric_utils/token_classification.py
imports: import typing
  from collections.abc import Sequence
  from seqeval.metrics import accuracy_score, f1_score, precision_score, recall_score
  from seqeval.scheme import Token
  from sklearn.metrics import make_scorer
  from deepchecks.core.errors import DeepchecksValueError
label_desc: Use image node_id 3 for calling a global function with example usage: validate_scorers(scorers) without return types